Another recent approach to bootstrapping that enables efficient implementations for big data is the so-called 'Bag of little bootstraps' [1]}. This approach splits the full-sample inference problem into several smaller inference problem, and then weights the results together in a consistent manner. Bag of little bootstraps is non-parametric in the sense that it applies to any estimator, while still allowing efficient parallelization implementations.
